# -*- coding: utf-8 -*-
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#作者：cacho_37967865
#博客：https://blog.csdn.net/sinat_37967865
#文件：tensorflow_mnist_cnn.py
#日期：2019-11-12
#备注：官方进阶教程：卷积神经网络算法（CNN）二层卷积、AdamOptimizer优化器
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import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

sTime = time.time()
mnist = input_data.read_data_sets('F:\PythonProject\Mnist', one_hot=True)
sess = tf.InteractiveSession()

# 实现卷积神经网络模型
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784,10]))            # 权重W
b = tf.Variable(tf.zeros([10]))                # 偏置b

y_ = tf.placeholder(tf.float32, [None, 10])    # 输入实际标签值


# 1.权重初始化：ReLU神经元
def weight_variable(shape):
  initial = tf.truncated_normal(shape, stddev=0.1)      # 权重
  return tf.Variable(initial)

def bias_variable(shape):
  initial = tf.constant(0.1, shape=shape)               # 偏置项
  return tf.Variable(initial)


# 2.卷积和池化
def conv2d(x, W):
  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')  # 卷积使用1步长（stride size），0边距（padding size）的模板

def max_pool_2x2(x):
  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],                    # 池化用简单传统的2x2大小的模板做max pooling
                        strides=[1, 2, 2, 1], padding='SAME')


# 3.第一层卷积：一个卷积接一个max pooling完成
filter = [5,5,1,32]                          # 5x5x1的卷积核,32个卷积核
W_conv1 = weight_variable(filter)
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1,28,28,1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)


# 4.第二层卷积：把几个类似的层堆叠起来
W_conv2 = weight_variable([5, 5, 32, 64])      # 第二层中，每个5x5的patch会得到64个特征：卷积核尺寸为5x5x32；32是通道数，64个卷积核
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)


# 5.第一个密集连接层
W_fc1 = weight_variable([7 * 7 * 64, 1024])   # 二层卷积：28/2/2=7
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)    # 使用1024个神经元，激发函数依然是Relu


# 6.Dropout：屏蔽神经元的输出外，还会自动处理神经元输出值的scale
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)


# 7.输出层：softmax层
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)  # 使用10个神经元，激发函数为softmax，用于输出结果


# 评估我们的模型
cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
#cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y_conv),reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)    # AdamOptimizer优化器
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

saver = tf.train.Saver()

# 训练模型：feed_dict中加入额外的参数keep_prob来控制dropout比例
sess.run(tf.global_variables_initializer())
for i in range(10000):
  batch = mnist.train.next_batch(50)
  if i%100 == 0:
    train_accuracy = accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})
    print("step %d, training accuracy %g" %(i, train_accuracy))
  train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

saver.save(sess, 'F:\PythonProject\Mnist\model\\cnn\mnist.ckpt')
print("test accuracy %g" %accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))

eTime = time.time()
s = eTime - sTime
print('花费的时间为：%.2f秒' % (s))